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Machine Learning-Based Prediction Model for Postpartum Pelvic Floor Hypertonicity Risk Model Construction

Machine Learning-Based Prediction Model for Postpartum Pelvic Floor Hypertonicity Risk Model Construction

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600117411
Enrollment
Unknown
Registered
2026-01-23
Start date
2025-11-10
Completion date
Unknown
Last updated
2026-01-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pelvic Floor Hypertonicity

Interventions

Pelvic Floor Hypertonic Group:None
Non-pelvic floor hypertonicity group:None

Sponsors

Shenzhen Hospital, Southern Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Age >= 18 years; 2. Within 6 weeks to 1 year postpartum; 3. Medical records containing comprehensive pelvic floor electromyography assessment or clinical diagnostic documentation.

Exclusion criteria

Exclusion criteria: 1. Serious deficiencies in medical records preclude access to required variables for the study; 2. Concurrent neurological conditions (e.g., spinal cord injury, multiple sclerosis) or pelvic organic lesions (e.g., tumours, severe deformities); 3. History of pelvic surgery (e.g., hysterectomy, pelvic floor reconstruction).

Design outcomes

Primary

MeasureTime frame
Accuracy;Recall;F1;Area under the subject curve;—

Countries

China

Contacts

Public ContactCai Wenzhi

Shenzhen Hospital, Southern Medical University

caiwzh@smu.edu.cn+86 755 2336 0006

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026